A computed tomography (CT) scanner uses X-rays to determine the three-dimensional structure of an object. X-ray beams (“rays”) are passed through the object from different angles, and “detector elements” (also known as “detectors”) on the other side measure the intensity of each attenuated ray. “Ray” can also refer to the path traversed by X-rays between the source and a single detector, the detector measurement, or to the ray sum (defined below). All of the detector measurements for a single fixed X-ray source and detector configuration are referred to as a “projection.” “Projection data” refers to the complete set, or a subset, of detector measurements. Detector measurements can also be expressed as “ray sums,” which provide information on the sum of the X-ray attenuation coefficients along each ray. “Ray sums” can also be obtained using other imaging modalities, such as positron emission tomography (PET), or single photon emission computed tomography (SPECT). For these other imaging modalities, “ray sum” refers to the sum of the emitter densities along a given path. The ray sums are then reconstructed into a three dimensional model (“CT image” or “reconstructed image”) of the object using a method such as filtered backprojection (FBP), an algebraic reconstruction algorithm such as the algebraic reconstruction technique (ART), or Fourier reconstruction. For CT images, “density” refers to the X-ray attenuation coefficient, which can also be expressed in Hounsfield units. For PET or SPECT images, “density” refers to the emitter density. The reconstructed image consists of a set of density elements, typically called pixels or voxels, which can be arranged in a regular or irregular grid in two or three dimensions.
Given exact projection data with infinite resolution, these methods can reconstruct the object perfectly. However, given noisy data with limited resolution or missing or corrupted data values, the reconstructed image can contain incorrect elements (“artifacts”) such as streaking or starburst patterns 10 (as shown in FIG. 3). This is particularly true around high density materials such as bone, metal, metal salts (such as barium sulfate), or iodinated contrast. These artifacts are typically caused by photon counting error (Poisson error), beam hardening effects, edge effects, patient motion, scatter, and other effects. The artifacts can obscure important information, possibly resulting in an incorrect or incomplete diagnosis.
Several strategies have been proposed to reduce artifacts in CT images. Photon counting noise can be reduced by increasing the tube current, which increases radiation exposure to the patient. A beam hardening correction can be applied as a pre-processing step, or as an iterative correction based on the current reconstructed image. The ART method can be modified to converge to a maximum likelihood (ML), maximum entropy, or minimum norm solution. Noisy projection data can be replaced with smoothed or interpolated data. Specifically, the linear interpolation (LI) technique erases the metal by replacing rays that pass through metal with values linearly interpolated from rays that pass adjacent to the metal, then uses FBP to reconstruct the image. (B. De Man, et al, “Reduction of metal streak artifacts in x-ray computed tomography using a transmission maximum a posteriori algorithm,” IEEE transactions on nuclear science, vol. 47, no. 3, pp. 977-81, June 2000; S. Vandenberghe, et al, “Iterative reconstruction algorithms in nuclear medicine,” Computerized medical imaging and graphics, vol. 25, pp. 105-11, 2001; D. Verhoeven, “Limited-data computed tomography algorithms for the physical sciences,” Applied optics, vol. 32, no. 20, pp. 3736-54, Jul. 10, 1993; G. Glover, et al, “An algorithm for the reduction of metal clip artifacts in CT reconstructions,” Medical Physics, vol. 8, pp. 799-807, November-December 1981; W. Kalender, et al, “Reduction of CT artifacts caused by metallic implants,” Radiology, vol. 164, pp. 576-77, August 1987.)
Some artifacts still remain after using these existing methods. For example, the maximum likelihood method tries to find an image that has the highest probability of generating the projection data, assuming that photon counts in each detector element follow a Poisson distribution. This ignores other sources of error, such as scatter, edge effects, or errors in the beam hardening correction. Furthermore, there are many images consistent with the projection data within experimental error, and the maximum likelihood method does not specify which image to pick. Thus, the final reconstructed image depends on the initial image, and how many maximum likelihood iterations are applied. Applying too many iterations results in overfitting and a noisier image. The linear interpolation method effectively erases the metal, and thus reduces streaking due to beam hardening and edge effects. However, it does not address Poisson errors for rays passing through soft tissue and bone. Furthermore, the linear interpolation process can introduce new artifacts (for example, streaks between metal and bone).
Here, we present a method for CT artifact reduction that addresses these issues. This method is called the metal deletion technique (MDT). Reducing artifacts results in clearer resolution images, less radiation use, and more accurate diagnosis.